Irina Barnaveli
PhD Student in Cognitive Neuroscience @doellerlab.bsky.social
- Reposted by Irina BarnaveliHow do we achieve few-shot generalization? New work led by @fabianrenz.bsky.social dives into the role of replay in learning and using structure to generalize reward. Dream team effort with Shany Grossman @nathanieldaw.bsky.social Peter Dayan & @doellerlab.bsky.social www.biorxiv.org/content/10.6...
- Reposted by Irina BarnaveliOur new paper, now published in Cell Reports, asks how the brain adaptively shapes its representations according to the statistical structure of the environment to overcome the limits of working memory capacity. www.cell.com/cell-reports...
- Reposted by Irina BarnaveliNew preprint: Inference over hidden contexts shapes the geometry of conceptual knowledge for flexible behaviour. In this pre-reg study, our core claim was that we don’t just learn stimulus-reward. We infer hidden context and that inference re-wires attention and neural state space on the fly. 1/8
- Reposted by Irina BarnaveliHappy to share this early Christmas present 🎄: our paper about geometry- and locomotion-dependence of 3D memory got published in PNAS! Joint work with co-first-author Volker Reisner (@reisnerv.bsky.social) as well as Leonard König, Misun Kim & Christian Doeller www.pnas.org/doi/10.1073/
- Reposted by Irina BarnaveliNew preprint! Have you ever wondered, what are these fuzzy simplicial sets, the theoretical framework behind e.g. UMAP? Here we show that you may simply see them as marginal distributions over simplicial sets. This provides a generative model for UMAP. (1/2) arxiv.org/abs/2512.03899
- Reposted by Irina BarnaveliOur new paper, now published in @natcomms.nature.com , asks a simple question: when two tasks share a common structure, does the brain learn them more efficiently? Surprisingly, this was not the case. Thread below (1/7) rdcu.be/eSwvU
- Reposted by Irina BarnaveliNew preprint from the lab and great work by Fei Wang. We show how subiculum trace vector cells can be modeled consistent with known effects in CA1. Traces are driven by a mismatch learning rule to keep associative memories in line with experience. www.biorxiv.org/content/10.1...
- Reposted by Irina Barnaveli🚨 New preprint! Impact of Task Similarity and Training Regimes on Cognitive Transfer and Interference 🧠 We compare humans and neural networks in a learning task, showing how training regime and task similarity interact to drive transfer or interference. www.biorxiv.org/content/10.1...
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- Reposted by Irina BarnaveliNew theory: Categorization is Baked Into the Brain Categorization is not a late stage of sensory processing but a core computation that begins at the earliest stages, occurring throughout the cortex as predictive feedback shapes feedforward processing. doi.org/10.31234/osf... #neuroscience
- Reposted by Irina Barnaveli🚨Preprint Alert: Who Does What in Deep Learning? Multidimensional Contribution of Neural Units using Game Theory: arxiv.org/abs/2506.19732 The result of my MSc thesis is out with @kayson.bsky.social @fatemehhadaeghi.bsky.social @patrickmineault.bsky.social @kordinglab.bsky.social, Claus C. Hilgetag
- Reposted by Irina BarnaveliExcited to share that our work on the interplay of symmetric learning rules and successor representations (SR) is published in @plos.org (comp.bio) Work done together with @doellerlab.bsky.social, @caswell.bsky.social and Juergen Jost. doi.org/10.1371/jour... #neuroskyence #compneurosky 1/n
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- Reposted by Irina BarnaveliNew preprint! We explored how learned movement patterns affect our sense of traveled distance and proposed a neurocomputational model that leverages embodied memories to denoise spatial codes. 🔗 www.biorxiv.org/cgi/content/...
- Reposted by Irina Barnaveli🧠 Can a short period of awake, quiet rest help you generalize better? In our new study, we tested this with a learning paradigm comparing offline vs online wake period. www.sciencedirect.com/science/arti...
